Improved Genetic Fuzzy Drivers presented at CIG 2018

Last week I presented at IEEE CIG 2018 (held in Maastricht, The Netherlands) our following step in our research about autonomous drivers for Car Racing Simulators, such as TORCS, titled “The Evolutionary Race: Improving the Process of Evaluating Car Controllers in Racing Simulators“.

As commented before by @jjmerelo and later by @fergunet, we designed with Mohammed Salem (University of Mascara) a driver’s AI in which two Fuzzy Subcontrollers were hybridized with a Genetic Algorithm.

In this work we present a better evaluation approach for the GA, combining three methods: heuristic track choosing, improved fitness functions, and race-based selection of the best.

The abstract of the work is:

Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.

And the presentation is:

You can check our paper in the proceedings of the conference.

Enjoy it!

(And cite us as usual :D)

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Our TORCS driving controller presented at EvoGAMES 2017

Last week, @jjmerelo presented at EvoGAMES 2017 (inside Evo* 2017) our work titled “Driving in TORCS using modular fuzzy controllers”.

This paper presents a novel car racing controller for TORCS (The Open Racing Car Simulator), which is based in the combination of two fuzzy subcontrollers, one for setting the speed, and one to control the steer angle. The obtained results are quite promissing, as the controller is quite competitive even against very tough TORCS teams.

The abstract of the paper is:

When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.

There was an interactive presentation at the conference, together with a poster:

The paper is available online from: https://link.springer.com/chapter/10.1007/978-3-319-55849-3_24

Enjoy (and cite) it! :D

 

Starcraft GP nominated to the HUMIES award

This year we participated in the HUMIES awards with our paper “Towards Automatic StarCraft Strategy Generation Using Genetic Programming“, accepted at CIG2015, wrote in collaboration with Politecnico di Torino and INRA. Our paper was elected from 28 candidates to be part of the 8 finalists, so we can consider it a great achievement. Although we didn’t won, because the astounding quality of the other works, we are thrilled about our nomination :)

Here is the presentation. It even includes a reference to Starship Troopers!

Towards automatic StarCraft strategy generation using genetic programming

I forgot to mention that we published our paper “Towards automatic StarCraft strategy generation using genetic programming” in CIG 2015 conference, held in Taiwan. This was a work made in collaboration with Alberto Tonda (INRA) and Giovanni Squillero (Politecnico di Torino), starting a new research line using this game (and also, starting other nice collaborations that are still a secret!)

The abstract:

Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or “bots”, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bot’s behavior during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.

Do you want to know more? Download the paper draft or electronic version in IEEE web.

Ms. PacMan in IEEE Transactions on CI and AI in Games

Our fans and followers must be happy! ;D

They can now access the excellent work by Federico Liberatore in IEEE ToCIAIG journal.

This is the best journal concerning Artificial Intelligence in games, with a very strict reviewing process, so, we are very proud of this success. ;)

This is the next step in the research started one year and a half ago designing competitive  Ghost Teams for catching Ms. PacMan.

The abstract is:

In the last year, thanks to the Ms. Pac-Man vs Ghosts competition, the game of Ms. Pac-Man has gained increasing attention from academics in the field of Computational Intelligence. In this work, we contribute to this research stream by presenting a simple Genetic Algorithm with Lexicographic Ranking (GALR) for the optimization of Flocking Strategy-based ghost controllers. Flocking Strategies are a paradigm for intelligent agents characterized by showing emergent behavior and for having very little computational and memory requirements, making them well suited for commercial applications and mobile devices. In particular, we study empirically the effect of optimizing homogeneous and heterogeneous teams. The computational analysis shows that the Flocking Strategy-based controllers generated by the proposed GALR outperform the ghost controllers included in the competition framework and some of those presented in the literature.

The paper can be found here: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7093170&tag=1

Enjoy it!

(And cite us) :D

[Paper] My life as a sim: evolving unique and engaging life stories using virtual worlds

Our latest publication My life as a sim: evolving unique and engaging life stories using virtual worlds, using our framework MADE (created by @rubenhek), has been published in the ALIFE 2014 conference. The abstract:

Stories are not only painfully weaved by crafty writers in the solitude of their studios; they also have to be produced massively for non-player characters in the video game industry or tailored to particular tastes in personalized stories. However, the creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work describes a general methodology to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) can emerge in complex stochastic systems. This methodology supports the modeling and parametrization of the agents, the environment where they will live and the desired literary setting. The use of a Genetic Algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the literary work. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results, testing with three different literary settings.

Check out the presentation by @jjmerelo at http://jj.github.io/alife14-made/#/home. You can download the proceedings of the conference (CC license), or download the paper draft.

More information is available on the project page.

Evolving Evil: Optimizing Flocking Strategies through Genetic Algorithms for the Ghost Team in the Game of Ms. Pac-Man

by Federico Liberatore, Antonio Mora, Pedro Castillo, Juan Julián Merelo in EvoGAMES
Flocking strategies are sets of behavior rules for the interaction of agents that allow to devise controllers with reduced complexity that generate emerging behavior. In this paper, we present an application of genetic algorithms and flocking strategies to control the Ghost Team in the game Ms. Pac-Man. In particular, we define flocking strategies for the Ghost Team and optimize them for robustness with respect to the stochastic elements of the game and effectivity against different possible opponents by means of genetic algorithm. 
The performance of the methodology proposed is tested and compared with that of other standard controllers belonging to the framework of the Ms. Pac-Man versus Ghosts Competition. The results show that flocking strategies are capable of modelling complex behaviors and produce effective and challenging agents. 

The presentation is:

 

You can also see a brief demo here (we are the ghosts :D):

Enjoy it!

(And cite us, of course :D)